Facial synthesis in augmented reality content for online communities

ABSTRACT

The subject technology captures first image data by a computing device, the first image data comprising a target face of a target actor and facial expressions of the target actor, the facial expressions including lip movements. The subject technology generates, based at least in part on frames of a source media content, sets of source pose parameters. The subject technology receives a selection of a particular facial expression from a set of facial expressions. The subject technology generates, based at least in part on sets of source pose parameters and the selection of the particular facial expression, an output media content. The subject technology provides augmented reality content based at least in part on the output media content for display on the computing device.

PRIORITY CLAIM

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 63/168,996, filed Mar. 31, 2021, which is herebyincorporated by reference herein in its entirety for all purposes.

BACKGROUND

With the increased use of digital images, affordability of portablecomputing devices, availability of increased capacity of digital storagemedia, and increased bandwidth and accessibility of network connections,digital images have become a part of the daily life for an increasingnumber of people.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexample embodiments.

FIG. 2 is a diagrammatic representation of a messaging clientapplication, in accordance with some example embodiments.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some example embodiments.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome example embodiments.

FIG. 5 is a schematic diagram illustrating a structure of the messageannotations, as described in FIG. 4 , including additional informationcorresponding to a given message, according to some embodiments.

FIG. 6 is a block diagram illustrating various modules of a messagingclient application, according to certain example embodiments.

FIG. 7 illustrates example interfaces (e.g., graphical user interface)in the subject messaging system, according to some embodiments.

FIG. 8 illustrates example interfaces (e.g., graphical user interface)in the subject messaging system, according to some embodiments.

FIG. 9 illustrates example interfaces (e.g., graphical user interface)in the subject messaging system, according to some embodiments.

FIG. 10 illustrates an interface (e.g., graphical user interface) in thesubject messaging system, according to some embodiments.

FIG. 11 is a flowchart illustrating a method, according to certainexample embodiments.

FIG. 12 is block diagram showing a software architecture within whichthe present disclosure may be implemented, in accordance with someexample embodiments.

FIG. 13 is a diagrammatic representation of a machine, in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed, in accordance with some example embodiments.

DETAILED DESCRIPTION

Users with a range of interests from various locations can capturedigital images of various subjects and make captured images available toothers via networks, such as the Internet. To enhance users' experienceswith digital images and provide various features, enabling computingdevices to perform image processing operations on various objects and/orfeatures captured in a wide range of changing conditions (e.g., changesin image scales, noises, lighting, movement, or geometric distortion)can be challenging and computationally intensive.

Augmented reality technology aims to bridge a gap between virtualenvironments and a real world environment by providing an enhanced realworld environment that is augmented with electronic information. As aresult, the electronic information appears to be part of the real worldenvironment as perceived by a user. In an example, augmented realitytechnology further provides a user interface to interact with theelectronic information that is overlaid in the enhanced real worldenvironment.

As mentioned above, with the increased use of digital images,affordability of portable computing devices, availability of increasedcapacity of digital storage media, and increased bandwidth andaccessibility of network connections, digital images have become a partof the daily life for an increasing number of people. Users with a rangeof interests from various locations can capture digital images ofvarious subjects and make captured images available to others vianetworks, such as the Internet. To enhance users' experiences withdigital images and provide various features, enabling computing devicesto perform image processing operations on various objects and/orfeatures captured in a wide range of changing conditions (e.g., changesin image scales, noises, lighting, movement, or geometric distortion)can be challenging and computationally intensive.

Messaging systems are frequently utilized and are increasingly leveragedby users of mobile computing devices, in various settings, to providedifferent types of functionality in a convenient manner. As describedherein, the subject messaging system comprises practical applicationsthat provide improvements in rendering augmented reality contentgenerators (e.g., providing augmented reality experiences) on mediacontent (e.g., images, videos, and the like) in which a particularaugmented reality content generator may be activated through an improvedsystem that enables providing augmented reality content that are moreadvantageously tailored for specific requirements associated with onlineadvertising campaigns of respective entities (e.g., merchants,companies, individuals, and the like).

Embodiments of the subject technology enable face animation synthesisthat may include transferring a facial expression of a source individualin a source video to a target individual in a target video or a targetimage. The face animation synthesis can be used for manipulation andanimation of faces in many applications, such as entertainment shows,computer games, video conversations, virtual reality, augmented reality,and the like.

Some current techniques for face animation synthesis utilize morphableface models to re-render the target face with a different facialexpression. While generation of a face with a morphable face model canbe fast, the generated face may not be photorealistic. Some othercurrent techniques for face animation synthesis are time-consuming andmay not be suitable to perform a real-time face animation synthesis onregular mobile devices.

Messaging systems are frequently utilized and are increasingly leveragedby users of mobile computing devices, in various settings, to providedifferent types of functionality in a convenient manner. As describedherein, the subject messaging system comprises practical applicationsthat provide improvements in capturing image data and rendering ARcontent (e.g., images, videos, and the like) based on the captured imagedata by at least providing technical improvements with capturing imagedata using power and resource constrained electronic devices. Suchimprovements in capturing image data are enabled by techniques providedby the subject technology, which reduce latency and increase efficiencyin processing captured image data thereby also reducing powerconsumption in the capturing devices.

As discussed further herein, the subject infrastructure supports thecreation and sharing of interactive media, referred to herein asmessages including 3D content or AR effects, throughout variouscomponents of a messaging system. In example embodiments describedherein, messages can enter the system from a live camera or via fromstorage (e.g., where messages including 3D content and/or AR effects arestored in memory or a database). The subject system supports motionsensor input, and loading of external effects and asset data.

As referred to herein, the phrase “augmented reality experience,”“augmented reality content item,” “augmented reality content generator”includes or refers to various image processing operations correspondingto an image modification, filter, AR content generators, media overlay,transformation, and the like, and additionally can include playback ofaudio or music content during presentation of AR content or mediacontent, as described further herein.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102 (e.g., a computing device), each of which hosts a number ofapplications including a messaging client application 104. Eachmessaging client application 104 is communicatively coupled to otherinstances of the messaging client application 104 and a messaging serversystem 108 via a network 106 (e.g., the Internet).

A messaging client application 104 is able to communicate and exchangedata with another messaging client application 104 and with themessaging server system 108 via the network 106. The data exchangedbetween messaging client application 104, and between a messaging clientapplication 104 and the messaging server system 108, includes functions(e.g., commands to invoke functions) as well as payload data (e.g.,text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, the location of certain functionalityeither within the messaging client application 104 or the messagingserver system 108 is a design choice. For example, it may be technicallypreferable to initially deploy certain technology and functionalitywithin the messaging server system 108, but to later migrate thistechnology and functionality to the messaging client application 104where a client device 102 has a sufficient processing capacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include, message content, client device information,geolocation information, media annotation and overlays, message contentpersistence conditions, social network information, and live eventinformation, as examples. Data exchanges within the messaging system 100are invoked and controlled through functions available via userinterfaces (UIs) of the messaging client application 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The Application Program Interface (API) server 110 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application server 112. Specifically, theApplication Program Interface (API) server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The Application Program Interface (API)server 110 exposes various functions supported by the application server112, including account registration, login functionality, the sending ofmessages, via the application server 112, from a particular messagingclient application 104 to another messaging client application 104, thesending of media files (e.g., images or video) from a messaging clientapplication 104 to the messaging server application 114, and forpossible access by another messaging client application 104, the settingof a collection of media data (e.g., story), the retrieval of a list offriends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the adding anddeletion of friends to a social graph, the location of friends within asocial graph, and opening an application event (e.g., relating to themessaging client application 104).

The application server 112 hosts a number of applications andsubsystems, including a messaging server application 114, an imageprocessing system 116 and a social network system 122. The messagingserver application 114 implements a number of message processingtechnologies and functions, particularly related to the aggregation andother processing of content (e.g., textual and multimedia content)included in messages received from multiple instances of the messagingclient application 104. As will be described in further detail, the textand media content from multiple sources may be aggregated intocollections of content (e.g., called stories or galleries). Thesecollections are then made available, by the messaging server application114, to the messaging client application 104. Other processor and memoryintensive processing of data may also be performed server-side by themessaging server application 114, in view of the hardware requirementsfor such processing.

The application server 112 also includes an image processing system 116that is dedicated to performing various image processing operations,typically with respect to images or video received within the payload ofa message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions services, and makes these functions and services available tothe messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph 304 (as shown in FIG.3 ) within the database 120. Examples of functions and servicessupported by the social network system 122 include the identification ofother users of the messaging system 100 with which a particular user hasrelationships or is ‘following’, and also the identification of otherentities and interests of a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of some subsystems, namely an ephemeral timer system 202, acollection management system 204 and an annotation system 206.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message, orcollection of messages (e.g., a story), selectively display and enableaccess to messages and associated content via the messaging clientapplication 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image video and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text and audio) may be organized into an ‘eventgallery’ or an ‘event story.’ Such a collection may be made availablefor a specified time period, such as the duration of an event to whichthe content relates. For example, content relating to a music concertmay be made available as a ‘story’ for the duration of that musicconcert. The collection management system 204 may also be responsiblefor publishing an icon that provides notification of the existence of aparticular collection to the user interface of the messaging clientapplication 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay or supplementation (e.g., an imagefilter) to the messaging client application 104 based on a geolocationof the client device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, texts, logos, animations, and sound effects. Anexample of a visual effect includes color overlaying. The audio andvisual content or the visual effects can be applied to a media contentitem (e.g., a photo) at the client device 102. For example, the mediaoverlay may include text that can be overlaid on top of a photographtaken by the client device 102. In another example, the media overlayincludes an identification of a location overlay (e.g., Venice beach), aname of a live event, or a name of a merchant overlay (e.g., BeachCoffee House). In another example, the annotation system 206 uses thegeolocation of the client device 102 to identify a media overlay thatincludes the name of a merchant at the geolocation of the client device102. The media overlay may include other indicia associated with themerchant. The media overlays may be stored in the database 120 andaccessed through the database server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map, and upload content associated with the selectedgeolocation. The user may also specify circumstances under which aparticular media overlay should be offered to other users. Theannotation system 206 generates a media overlay that includes theuploaded content and associates the uploaded content with the selectedgeolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest bidding merchant with a corresponding geolocationfor a predefined amount of time.

FIG. 3 is a schematic diagram illustrating data structures 300 which maybe stored in the database 120 of the messaging server system 108,according to certain example embodiments. While the content of thedatabase 120 is shown to comprise a number of tables, it will beappreciated that the data could be stored in other types of datastructures (e.g., as an object-oriented database).

The database 120 includes message data stored within a message table314. The entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, etc. Regardless of type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between entities. Such relationships maybe social, professional (e.g., work at a common corporation ororganization) interested-based or activity-based, merely for example.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of variestypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters) which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a GPS unit of the client device102. Another type of filer is a data filer, which may be selectivelypresented to a sending user by the messaging client application 104,based on other inputs or information gathered by the client device 102during the message creation process. Example of data filters includecurrent temperature at a specific location, a current speed at which asending user is traveling, battery life for a client device 102, or thecurrent time.

Other annotation data that may be stored within the image table 308 areaugmented reality content generators (e.g., corresponding to applying ARcontent generators, augmented reality experiences, or augmented realitycontent items). An augmented reality content generator may be areal-time special effect and sound that may be added to an image or avideo.

As described above, augmented reality content generators, augmentedreality content items, overlays, image transformations, AR images andsimilar terms refer to modifications that may be made to videos orimages. This includes real-time modification which modifies an image asit is captured using a device sensor and then displayed on a screen ofthe device with the modifications. This also includes modifications tostored content, such as video clips in a gallery that may be modified.For example, in a device with access to multiple augmented realitycontent generators, a user can use a single video clip with multipleaugmented reality content generators to see how the different augmentedreality content generators will modify the stored clip. For example,multiple augmented reality content generators that apply differentpseudorandom movement models can be applied to the same content byselecting different augmented reality content generators for thecontent. Similarly, real-time video capture may be used with anillustrated modification to show how video images currently beingcaptured by sensors of a device would modify the captured data. Suchdata may simply be displayed on the screen and not stored in memory, orthe content captured by the device sensors may be recorded and stored inmemory with or without the modifications (or both). In some systems, apreview feature can show how different augmented reality contentgenerators will look within different windows in a display at the sametime. This can, for example, enable multiple windows with differentpseudorandom animations to be viewed on a display at the same time.

Data and various systems using augmented reality content generators orother such transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousembodiments, different methods for achieving such transformations may beused. For example, some embodiments may involve generating athree-dimensional mesh model of the object or objects, and usingtransformations and animated textures of the model within the video toachieve the transformation. In other embodiments, tracking of points onan object may be used to place an image or texture (which may be twodimensional or three dimensional) at the tracked position. In stillfurther embodiments, neural network analysis of video frames may be usedto place images, models, or textures in content (e.g., images or framesof video). Augmented reality content generators thus refer both to theimages, models, and textures used to create transformations in content,as well as to additional modeling and analysis information needed toachieve such transformations with object detection, tracking, andplacement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some embodiments, when a particular modification is selected alongwith content to be transformed, elements to be transformed areidentified by the computing device, and then detected and tracked ifthey are present in the frames of the video. The elements of the objectare modified according to the request for modification, thustransforming the frames of the video stream. Transformation of frames ofa video stream can be performed by different methods for different kindsof transformation. For example, for transformations of frames mostlyreferring to changing forms of object's elements characteristic pointsfor each of element of an object are calculated (e.g., using an ActiveShape Model (ASM) or other known methods). Then, a mesh based on thecharacteristic points is generated for each of the at least one elementof the object. This mesh used in the following stage of tracking theelements of the object in the video stream. In the process of tracking,the mentioned mesh for each element is aligned with a position of eachelement. Then, additional points are generated on the mesh. A first setof first points is generated for each element based on a request formodification, and a set of second points is generated for each elementbased on the set of first points and the request for modification. Then,the frames of the video stream can be transformed by modifying theelements of the object on the basis of the sets of first and secondpoints and the mesh. In such method, a background of the modified objectcan be changed or distorted as well by tracking and modifying thebackground.

In one or more embodiments, transformations changing some areas of anobject using its elements can be performed by calculating ofcharacteristic points for each element of an object and generating amesh based on the calculated characteristic points. Points are generatedon the mesh, and then various areas based on the points are generated.The elements of the object are then tracked by aligning the area foreach element with a position for each of the at least one element, andproperties of the areas can be modified based on the request formodification, thus transforming the frames of the video stream.Depending on the specific request for modification properties of thementioned areas can be transformed in different ways. Such modificationsmay involve changing color of areas; removing at least some part ofareas from the frames of the video stream; including one or more newobjects into areas which are based on a request for modification; andmodifying or distorting the elements of an area or object. In variousembodiments, any combination of such modifications or other similarmodifications may be used. For certain models to be animated, somecharacteristic points can be selected as control points to be used indetermining the entire state-space of options for the model animation.

In some embodiments of a computer animation model to transform imagedata using face detection, the face is detected on an image with use ofa specific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

In other embodiments, other methods and algorithms suitable for facedetection can be used. For example, in some embodiments, features arelocated using a landmark which represents a distinguishable pointpresent in most of the images under consideration. For facial landmarks,for example, the location of the left eye pupil may be used. In aninitial landmark is not identifiable (e.g., if a person has aneyepatch), secondary landmarks may be used. Such landmark identificationprocedures may be used for any such objects. In some embodiments, a setof landmarks forms a shape. Shapes can be represented as vectors usingthe coordinates of the points in the shape. One shape is aligned toanother with a similarity transform (allowing translation, scaling, androtation) that minimizes the average Euclidean distance between shapepoints. The mean shape is the mean of the aligned training shapes.

In some embodiments, a search for landmarks from the mean shape alignedto the position and size of the face determined by a global facedetector is started. Such a search then repeats the steps of suggestinga tentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable and the shapemodel pools the results of the weak template matchers to form a strongeroverall classifier. The entire search is repeated at each level in animage pyramid, from coarse to fine resolution.

Embodiments of a transformation system can capture an image or videostream on a client device (e.g., the client device 102) and performcomplex image manipulations locally on the client device 102 whilemaintaining a suitable user experience, computation time, and powerconsumption. The complex image manipulations may include size and shapechanges, emotion transfers (e.g., changing a face from a frown to asmile), state transfers (e.g., aging a subject, reducing apparent age,changing gender), style transfers, graphical element application, andany other suitable image or video manipulation implemented by aconvolutional neural network that has been configured to executeefficiently on the client device 102.

In some example embodiments, a computer animation model to transformimage data can be used by a system where a user may capture an image orvideo stream of the user (e.g., a selfie) using a client device 102having a neural network operating as part of a messaging clientapplication 104 operating on the client device 102. The transform systemoperating within the messaging client application 104 determines thepresence of a face within the image or video stream and providesmodification icons associated with a computer animation model totransform image data, or the computer animation model can be present asassociated with an interface described herein. The modification iconsinclude changes which may be the basis for modifying the user's facewithin the image or video stream as part of the modification operation.Once a modification icon is selected, the transform system initiates aprocess to convert the image of the user to reflect the selectedmodification icon (e.g., generate a smiling face on the user). In someembodiments, a modified image or video stream may be presented in agraphical user interface displayed on the mobile client device as soonas the image or video stream is captured and a specified modification isselected. The transform system may implement a complex convolutionalneural network on a portion of the image or video stream to generate andapply the selected modification. That is, the user may capture the imageor video stream and be presented with a modified result in real time ornear real time once a modification icon has been selected. Further, themodification may be persistent while the video stream is being capturedand the selected modification icon remains toggled. Machine taughtneural networks may be used to enable such modifications.

In some embodiments, the graphical user interface, presenting themodification performed by the transform system, may supply the user withadditional interaction options. Such options may be based on theinterface used to initiate the content capture and selection of aparticular computer animation model (e.g., initiation from a contentcreator user interface). In various embodiments, a modification may bepersistent after an initial selection of a modification icon. The usermay toggle the modification on or off by tapping or otherwise selectingthe face being modified by the transformation system and store it forlater viewing or browse to other areas of the imaging application. Wheremultiple faces are modified by the transformation system, the user maytoggle the modification on or off globally by tapping or selecting asingle face modified and displayed within a graphical user interface. Insome embodiments, individual faces, among a group of multiple faces, maybe individually modified or such modifications may be individuallytoggled by tapping or selecting the individual face or a series ofindividual faces displayed within the graphical user interface.

In some example embodiments, a graphical processing pipelinearchitecture is provided that enables different augmented realityexperiences (e.g., AR content generators) to be applied in correspondingdifferent layers. Such a graphical processing pipeline provides anextensible rendering engine for providing multiple augmented realityexperiences that are included in a composite media (e.g., image orvideo) or composite AR content for rendering by the messaging clientapplication 104 (or the messaging system 100).

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe entity table 302. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 302). A user may createa ‘personal story’ in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a ‘live story,’ which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a ‘live story’ may constitute a curated stream of user-submitted contentfrom varies locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104, based on his or herlocation. The end result is a ‘live story’ told from a communityperspective.

A further type of content collection is known as a ‘location story’,which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 or the messaging client application 104 forcommunication to a further messaging client application 104 or themessaging server application 114. The content of a particular message400 is used to populate the message table 314 stored within the database120, accessible by the messaging server application 114. Similarly, thecontent of a message 400 is stored in memory as ‘in-transit’ or‘in-flight’ data of the client device 102 or the application server 112.The message 400 is shown to include the following components:

A message identifier 402: a unique identifier that identifies themessage 400.

A message text payload 404: text, to be generated by a user via a userinterface of the client device 102 and that is included in the message400.

A message image payload 406: image data, captured by a camera componentof a client device 102 or retrieved from a memory component of a clientdevice 102, and that is included in the message 400.

A message video payload 408: video data, captured by a camera componentor retrieved from a memory component of the client device 102 and thatis included in the message 400.

A message audio payload 410: audio data, captured by a microphone orretrieved from a memory component of the client device 102, and that isincluded in the message 400.

A message annotations 412: annotation data (e.g., filters, stickers orother enhancements) that represents annotations to be applied to messageimage payload 406, message video payload 408, or message audio payload410 of the message 400.

A message duration parameter 414: parameter value indicating, inseconds, the amount of time for which content of the message (e.g., themessage image payload 406, message video payload 408, message audiopayload 410) is to be presented or made accessible to a user via themessaging client application 104.

A message geolocation parameter 416: geolocation data (e.g., latitudinaland longitudinal coordinates) associated with the content payload of themessage. Multiple message geolocation parameter 416 values may beincluded in the payload, each of these parameter values being associatedwith respect to content items included in the content (e.g., a specificimage into within the message image payload 406, or a specific video inthe message video payload 408).

A message story identifier 418: identifier values identifying one ormore content collections (e.g., ‘stories’) with which a particularcontent item in the message image payload 406 of the message 400 isassociated. For example, multiple images within the message imagepayload 406 may each be associated with multiple content collectionsusing identifier values.

A message tag 420: each message 400 may be tagged with multiple tags,each of which is indicative of the subject matter of content included inthe message payload. For example, where a particular image included inthe message image payload 406 depicts an animal (e.g., a lion), a tagvalue may be included within the message tag 420 that is indicative ofthe relevant animal. Tag values may be generated manually, based on userinput, or may be automatically generated using, for example, imagerecognition.

A message sender identifier 422: an identifier (e.g., a messaging systemidentifier, email address, or device identifier) indicative of a user ofthe client device 102 on which the message 400 was generated and fromwhich the message 400 was sent

A message receiver identifier 424: an identifier (e.g., a messagingsystem identifier, email address, or device identifier) indicative of auser of the client device 102 to which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 308.Similarly, values within the message video payload 408 may point to datastored within a video table 310, values stored within the messageannotations 412 may point to data stored in an annotation table 312,values stored within the message story identifier 418 may point to datastored in a story table 306, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 302.

As described above, media overlays, such as AR content generators,overlays, image transformations, AR images and similar terms refer tomodifications that may be made to videos or images. This includesreal-time modification which modifies an image as it is captured using adevice sensor and then displayed on a screen of the device with themodifications. This also includes modifications to stored content, suchas video clips in a gallery that may be modified. For example, in adevice with access to multiple media overlays (e.g., AR contentgenerators), a user can use a single video clip with multiple AR contentgenerators to see how the different AR content generators will modifythe stored clip. For example, multiple AR content generators that applydifferent pseudorandom movement models can be applied to the samecontent by selecting different AR content generators for the content.Similarly, real-time video capture may be used with an illustratedmodification to show how video images currently being captured bysensors of a device would modify the captured data. Such data may simplybe displayed on the screen and not stored in memory, or the contentcaptured by the device sensors may be recorded and stored in memory withor without the modifications (or both). In some systems, a previewfeature can show how different AR content generators will look withindifferent windows in a display at the same time. This can, for example,enable multiple windows with different pseudorandom animations to beviewed on a display at the same time.

Data and various systems to use AR content generators or other suchtransform systems to modify content using this data can thus involvedetection of objects (e.g. faces, hands, bodies, cats, dogs, surfaces,objects, etc.), tracking of such objects as they leave, enter, and movearound the field of view in video frames, and the modification ortransformation of such objects as they are tracked. In variousembodiments, different methods for achieving such transformations may beused. For example, some embodiments may involve generating athree-dimensional mesh model of the object or objects, and usingtransformations and animated textures of the model within the video toachieve the transformation. In other embodiments, tracking of points onan object may be used to place an image or texture (which may be twodimensional or three dimensional) at the tracked position. In stillfurther embodiments, neural network analysis of video frames may be usedto place images, models, or textures in content (e.g. images or framesof video). Lens data thus refers both to the images, models, andtextures used to create transformations in content, as well as toadditional modeling and analysis information needed to achieve suchtransformations with object detection, tracking, and placement.

Real time video processing can be performed with any kind of video data,(e.g. video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some embodiments, when a particular modification is selected alongwith content to be transformed, elements to be transformed areidentified by the computing device, and then detected and tracked ifthey are present in the frames of the video. The elements of the objectare modified according to the request for modification, thustransforming the frames of the video stream. Transformation of frames ofa video stream can be performed by different methods for different kindsof transformation. For example, for transformations of frames mostlyreferring to changing forms of object's elements characteristic pointsfor each of element of an object are calculated (e.g. using an ActiveShape Model (ASM) or other known methods). Then, a mesh based on thecharacteristic points is generated for each of the at least one elementof the object. This mesh used in the following stage of tracking theelements of the object in the video stream. In the process of tracking,the mentioned mesh for each element is aligned with a position of eachelement. Then, additional points are generated on the mesh. A first setof first points is generated for each element based on a request formodification, and a set of second points is generated for each elementbased on the set of first points and the request for modification. Then,the frames of the video stream can be transformed by modifying theelements of the object on the basis of the sets of first and secondpoints and the mesh. In such method a background of the modified objectcan be changed or distorted as well by tracking and modifying thebackground.

In one or more embodiments, transformations changing some areas of anobject using its elements can be performed by calculating ofcharacteristic points for each element of an object and generating amesh based on the calculated characteristic points. Points are generatedon the mesh, and then various areas based on the points are generated.The elements of the object are then tracked by aligning the area foreach element with a position for each of the at least one element, andproperties of the areas can be modified based on the request formodification, thus transforming the frames of the video stream.Depending on the specific request for modification properties of thementioned areas can be transformed in different ways. Such modificationsmay involve: changing color of areas; removing at least some part ofareas from the frames of the video stream; including one or more newobjects into areas which are based on a request for modification; andmodifying or distorting the elements of an area or object. In variousembodiments, any combination of such modifications or other similarmodifications may be used. For certain models to be animated, somecharacteristic points can be selected as control points to be used indetermining the entire state-space of options for the model animation.

In some embodiments of a computer animation model to transform imagedata using face detection, the face is detected on an image with use ofa specific face detection algorithm (e.g. Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

In other embodiments, other methods and algorithms suitable for facedetection can be used. For example, in some embodiments, features arelocated using a landmark which represents a distinguishable pointpresent in most of the images under consideration. For facial landmarks,for example, the location of the left eye pupil may be used. In aninitial landmark is not identifiable (e.g. if a person has an eyepatch),secondary landmarks may be used. Such landmark identification proceduresmay be used for any such objects. In some embodiments, a set oflandmarks forms a shape. Shapes can be represented as vectors using thecoordinates of the points in the shape. One shape is aligned to anotherwith a similarity transform (allowing translation, scaling, androtation) that minimizes the average Euclidean distance between shapepoints. The mean shape is the mean of the aligned training shapes.

In some embodiments, a search for landmarks from the mean shape alignedto the position and size of the face determined by a global facedetector is started. Such a search then repeats the steps of suggestinga tentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable and the shapemodel pools the results of the weak template matchers to form a strongeroverall classifier. The entire search is repeated at each level in animage pyramid, from coarse to fine resolution.

Embodiments of a transformation system can capture an image or videostream on a client device and perform complex image manipulationslocally on a client device such as client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on aclient device.

In some example embodiments, a computer animation model to transformimage data can be used by a system where a user may capture an image orvideo stream of the user (e.g., a selfie) using a client device 102having a neural network operating as part of a messaging clientapplication 104 operating on the client device 102. The transform systemoperating within the messaging client application 104 determines thepresence of a face within the image or video stream and providesmodification icons associated with a computer animation model totransform image data, or the computer animation model can be present asassociated with an interface described herein. The modification iconsinclude changes which may be the basis for modifying the user's facewithin the image or video stream as part of the modification operation.Once a modification icon is selected, the transform system initiates aprocess to convert the image of the user to reflect the selectedmodification icon (e.g., generate a smiling face on the user). In someembodiments, a modified image or video stream may be presented in agraphical user interface displayed on the mobile client device as soonas the image or video stream is captured and a specified modification isselected. The transform system may implement a complex convolutionalneural network on a portion of the image or video stream to generate andapply the selected modification. That is, the user may capture the imageor video stream and be presented with a modified result in real time ornear real time once a modification icon has been selected. Further, themodification may be persistent while the video stream is being capturedand the selected modification icon remains toggled. Machine taughtneural networks may be used to enable such modifications.

In some embodiments, the graphical user interface, presenting themodification performed by the transform system, may supply the user withadditional interaction options. Such options may be based on theinterface used to initiate the content capture and selection of aparticular computer animation model (e.g. initiation from a contentcreator user interface). In various embodiments, a modification may bepersistent after an initial selection of a modification icon. The usermay toggle the modification on or off by tapping or otherwise selectingthe face being modified by the transformation system. and store it forlater viewing or browse to other areas of the imaging application. Wheremultiple faces are modified by the transformation system, the user maytoggle the modification on or off globally by tapping or selecting asingle face modified and displayed within a graphical user interface. Insome embodiments, individual faces, among a group of multiple faces, maybe individually modified or such modifications may be individuallytoggled by tapping or selecting the individual face or a series ofindividual faces displayed within the graphical user interface.

In some example embodiments, a graphical processing pipelinearchitecture is provided that enables different media overlays to beapplied in corresponding different layers. Such a graphical processingpipeline provides an extensible rendering engine for providing multipleaugmented reality content generators that are included in a compositemedia (e.g., image or video) or composite AR content for rendering bythe messaging client application 104 (or the messaging system 100).

As discussed herein, the subject infrastructure supports the creationand sharing of interactive messages with interactive effects throughoutvarious components of the messaging system 100. In an example, toprovide such interactive effects, a given interactive message mayinclude image data along with 2D data, or 3D data. The infrastructure asdescribed herein enables other forms of 3D and interactive media (e.g.,2D media content) to be provided across the subject system, which allowsfor such interactive media to be shared across the messaging system 100and alongside photo and video messages. In example embodiments describedherein, messages can enter the system from a live camera or via fromstorage (e.g., where messages with 2D or 3D content or augmented reality(AR) effects (e.g., 3D effects, or other interactive effects are storedin memory or a database). In an example of an interactive message with3D data, the subject system supports motion sensor input and manages thesending and storage of 3D data, and loading of external effects andasset data.

As mentioned above, an interactive message includes an image incombination with a 2D effect, or a 3D effect and depth data. In anexample embodiment, a message is rendered using the subject system tovisualize the spatial detail/geometry of what the camera sees, inaddition to a traditional image texture. When a viewer interacts withthis message by moving a client device, the movement triggerscorresponding changes in the perspective the image and geometry arerendered at to the viewer.

In an embodiment, the subject system provides AR effects (which mayinclude 3D effects using 3D data, or interactive 2D effects that do notuse 3D data) that work in conjunction with other components of thesystem to provide particles, shaders, 2D assets and 3D geometry that caninhabit different 3D-planes within messages. The AR effects as describedherein, in an example, are rendered in a real-time manner for the user.

As mentioned herein, a gyro-based interaction refers to a type ofinteraction in which a given client device's rotation is used as aninput to change an aspect of the effect (e.g., rotating phone alongx-axis in order to change the color of a light in the scene).

As mentioned herein, an augmented reality content generator refers to areal-time special effect and/or sound that may be added to a message andmodifies image and/or 3D data with an AR effects and/other 3D contentsuch as 3D animated graphical elements, 3D objects (e.g., non-animated),and the like.

The following discussion relates to example data that is stored inconnection with such a message in accordance to some embodiments.

FIG. 5 is a schematic diagram illustrating a structure of the messageannotations 412, as described above in FIG. 4 , including additionalinformation corresponding to a given message, according to someembodiments, generated by the messaging client application 104 or themessaging client application 104.

In an embodiment, the content of a particular message 400, as shown inFIG. 3 , including the additional data shown in FIG. 5 is used topopulate the message table 314 stored within the database 120 for agiven message, which is then accessible by the messaging clientapplication 104. As illustrated in FIG. 5 , message annotations 412includes the following components corresponding to various data:

-   -   augmented reality (AR) content identifier 552: identifier of an        AR content generator utilized in the message    -   message identifier 554: identifier of the message    -   asset identifiers 556: a set of identifiers for assets in the        message. For example, respective asset identifiers can be        included for assets that are determined by the particular AR        content generator. In an embodiment, such assets are created by        the AR content generator on the sender side client device,        uploaded to the messaging server application 114, and utilized        on the receiver side client device in order to recreate the        message. Examples of typical assets include:        -   The original still RGB image(s) captured by the camera        -   The post-processed image(s) with AR content generator            effects applied to the original image    -   augmented reality (AR) content metadata 558: additional metadata        associated with the AR content generator corresponding to the AR        identifier 552, such as:        -   AR content generator category: corresponding to a type or            classification for a particular AR content generator        -   AR content generator carousel index        -   carousel group: This can be populated and utilized when            eligible post-capture AR content generators are inserted            into a carousel interface. In an implementation, a new value            “AR_DEFAULT_GROUP” (e.g., a default group assigned to an AR            content generator can be added to the list of valid group            names.    -   capture metadata 560 corresponding to additional metadata, such        as:        -   camera image metadata            -   camera intrinsic data                -   focal length                -   principal point            -   other camera information (e.g., camera position)        -   sensor information            -   gyroscopic sensor data            -   position sensor data            -   accelerometer sensor data            -   other sensor data            -   location sensor data

FIG. 6 is a block diagram illustrating various modules of a messagingclient application 104, according to certain example embodiments. Themessaging client application 104 is shown as including an AR contentsystem 600. As further shown, the AR content system 600 includes acamera module 602, a capture module 604, an image data processing module606, a rendering module 608, and a content recording module 610. Thevarious modules of the AR content system 600 are configured tocommunicate with each other (e.g., via a bus, shared memory, or aswitch). Any one or more of these modules may be implemented using oneor more computer processors 620 (e.g., by configuring such one or morecomputer processors to perform functions described for that module) andhence may include one or more of the computer processors 620 (e.g., aset of processors provided by the client device 102).

Any one or more of the modules described may be implemented usinghardware alone (e.g., one or more of the computer processors 620 of amachine (e.g., machine 1300) or a combination of hardware and software.For example, any described module of the messaging client application104 may physically include an arrangement of one or more of the computerprocessors 620 (e.g., a subset of or among the one or more computerprocessors of the machine (e.g., machine 1300) configured to perform theoperations described herein for that module. As another example, anymodule of the AR content system 600 may include software, hardware, orboth, that configure an arrangement of one or more computer processors620 (e.g., among the one or more computer processors of the machine(e.g., machine 1300) to perform the operations described herein for thatmodule. Accordingly, different modules of the AR content system 600 mayinclude and configure different arrangements of such computer processors620 or a single arrangement of such computer processors 620 at differentpoints in time. Moreover, any two or more modules of the messagingclient application 104 may be combined into a single module, and thefunctions described herein for a single module may be subdivided amongmultiple modules. Furthermore, according to various example embodiments,modules described herein as being implemented within a single machine,database, or device may be distributed across multiple machines,databases, or devices.

The camera module 602 performs camera related operations, includingfunctionality for operations involving one or more cameras of the clientdevice 102. In an example, camera module 602 can access camerafunctionality across different processes that are executing on theclient device 102, determining surfaces for face or surface tracking,responding to various requests (e.g., involving image data of aparticular resolution or format) for camera data or image data (e.g.,frames) from such processes, providing metadata to such processes thatare consuming the requested camera data or image data. As mentionedherein, a “process” or “computing process” can refer to an instance of acomputer program that is being executed by one or more threads of agiven processor(s).

As mentioned herein, surface tracking refers to operations for trackingone or more representations of surfaces corresponding to planes (e.g., agiven horizontal plane, a floor, a table) in the input frame. In anexample, surface tracking is accomplished using hit testing and/or raycasting techniques. Hit testing, in an example, determines whether aselected point (e.g., pixel or set of pixels) in the input frameintersects with a surface or plane of a representation of a physicalobject in the input frame. Ray casting, in an example, utilizes aCartesian based coordinate system (e.g., x and y coordinates) andprojects a ray (e.g., vector) into the camera's view of the world, ascaptured in the input frame, to detect planes that the ray intersects.

As further illustrated, the camera module 602 receives the input frame(or alternatively a duplicate of the input frame in an embodiment). Thecamera module 602 can include various tracking functionality based on atype of object to track. In an example, the camera module 602 includestracking capabilities for surface tracking, face tracking, objecttracking, and the like. In an implementation, the camera module 602 mayonly execute one of each of a plurality of tracking processes at a timefor facilitating the management of computing resources at the clientdevice 102 or client device 102. In addition, the camera module 602 mayperform one or more object recognition or detection operations on theinput frame.

As referred to herein, tracking refers to operations for determiningspatial properties (e.g., position and/or orientation) of a given object(or portion thereof) during a post-processing stage. In animplementation, during tracking, the object's position and orientationare measured in a continuous manner. Different objects may be tracked,such as a user's head, eyes, or limbs, surfaces, or other objects.Tracking involves dynamic sensing and measuring to enable virtualobjects and/or effects to be rendered with respect to physical objectsin a three-dimensional space corresponding to a scene (e.g., the inputframe). Thus, the camera module 602 determines metrics corresponding toat least the relative position and orientation of one or more physicalobjects in the input frame and includes these metrics in tracking datawhich is provided to the rendering module 608. In an example, the cameramodule 602 updates (e.g., track over time) such metrics from frame tosubsequent frame.

In an implementation, the camera module 602 provides, as output,tracking data (e.g., metadata) corresponding to the aforementionedmetrics (e.g., position and orientation). In some instances, the cameramodule 602 includes logic for shape recognition, edge detection, or anyother suitable object detection mechanism. The object of interest mayalso be determined by the camera module 602 to be an example of apredetermined object type, matching shapes, edges, or landmarks within arange to an object type of a set of predetermined object types.

In an implementation, the camera module 602 can utilize techniques whichcombines information from the device's motion sensors (e.g.,accelerometer and gyroscope sensors, and the like) with an analysis ofthe scene provided in the input frame. For example, the camera module602 detects features in the input frame, and as a result, tracksdifferences in respective positions of such features across severalinput frames using information derived at least in part on data from themotion sensors of the device.

As mentioned herein, face tracking refers to operations for trackingrepresentations of facial features, such as portions of a user's face,in the input frame. In some embodiments, the camera module 602 includesfacial tracking logic to identify all or a portion of a face within theone or more images and track landmarks of the face across the set ofimages of the video stream. As mentioned herein, object tracking refersto tracking a representation of a physical object in the input frame.

In an embodiment, the camera module 602 utilizes machine learningtechniques to detect whether a physical object, corresponding to arepresentation of display screen, is included in captured image data(e.g., from a current field of view of the client device 102).

In an example, the camera module 602 utilizes a machine learning modelsuch a neural network is utilized for detecting a representation of adisplay screen in the image data. A neural network model can refer to afeedforward deep neural network that is implemented to approximate afunction ƒ Models in this regard are referred to as feedforward becauseinformation flows through the function being evaluated from an input x,through one or more intermediate operations used to define ƒ, andfinally to an output y. Feedforward deep neural networks are callednetworks because they may be represented by connecting togetherdifferent operations. A model of the feedforward deep neural networksmay be represented as a graph representing how the operations areconnected together from an input layer, through one or more hiddenlayers, and finally to an output layer. Each node in such a graphrepresents an operation to be performed in an example. It isappreciated, however, that other types of neural networks arecontemplated by the implementations described herein. For example, arecurrent neural network such as a long short-term memory (LS™) neuralnetwork may be provided for annotation, or a convolutional neuralnetwork (CNN) may be utilized.

In an example, for computer vision techniques of the subject technology,the camera module 602 utilizes a convolutional neural network model todetect a representation of a display screen (or other applicableobjects) in the image data. Such a convolutional neural network (CNN)can be trained using training data which includes thousands or millionsof images of display screens such that the trained CNN can be providedwith input data (e.g., image or video data) and perform tasks to detectthe presence of a display screen(s) in the input data. A convolutionoperation involves finding local patterns in the input data, such asimage data. Such patterns that are learned by the CNN therefore can berecognized in any other part of the image data, which advantageouslyprovides translation invariant capabilities. For example, an image of adisplay screen viewed from the side can still produce a correctclassification of a display screen as if the display screen was viewedfrontally. Similarly, in cases of occlusion when an object (e.g.,display screen) to be detected is partially blocked from view, the CNNis still able to detect the object in the image data.

In an embodiment, the camera module 602 acts as an intermediary betweenother components of the AR content system 600 and the capture module604. As mentioned above, the camera module 602 can receive requests forcaptured image data from the image data processing module 606. Thecamera module 602 can also receive requests for the captured image datafrom the content recording module 610. The camera module 602 can forwardsuch requests to the capture module 604 for processing.

The capture module 604 captures images (which may also include depthdata) captured by one or more cameras of client device 102 (e.g., inresponse to the aforementioned requests from other components). Forexample, an image is a photograph captured by an optical sensor (e.g.,camera) of the client device 102. An image includes one or morereal-world features, such as a user's face or real-world object(s)detected in the image. In some embodiments, an image includes metadatadescribing the image. Each captured image can be included in a datastructure mentioned herein as a “frame”, which can include the raw imagedata along with metadata and other information. In an embodiment,capture module 604 can send captured image data and metadata as(captured) frames to one or more components of the AR content system600.

The image data processing module 606 generates tracking data and othermetadata for captured image data, including metadata associated withoperations for generating AR content and AR effects applied to thecaptured image data. The image data processing module 606 performsoperations on the received image data. For example, various imageprocessing operations are performed by the image data processing module606. The image data processing module 606 performs various operationsbased on algorithms or techniques that correspond to animations and/orproviding visual and/or auditory effects to the received image data. Inan embodiment, a given augmented reality content generator can utilizethe image data processing module 606 to perform operations as part ofgenerating AR content and AR effects which is then provided to arendering process to render such AR content and AR effects (e.g.,including 2D effects or 3D effects) and the like.

The rendering module 608 performs rendering of AR content for display bythe messaging client application 104 based on data provided by at leastone of the aforementioned modules. In an example, the rendering module608 utilizes a graphical processing pipeline to perform graphicaloperations to render the AR content for display. The rendering module608 implements, in an example, an extensible rendering engine whichsupports multiple image processing operations corresponding torespective augmented reality content generators. In an example, therendering module 608 can receive a composite AR content for rendering ona display provided by client device 102.

In some implementations, the rendering module 608 provide a graphicssystem that renders two-dimensional (2D) objects or objects from athree-dimensional (3D) world (real or imaginary) onto a 2D displayscreen. Such a graphics system (e.g., one included on the client device102) includes a graphics processing unit (GPU) in some implementationsfor performing image processing operations and rendering graphicalelements for display.

In an implementation, the GPU includes a logical graphical processingpipeline, which can receive a representation of a 2D or 3D scene andprovide an output of a bitmap that represents a 2D image for display.Existing application programming interfaces (APIs) have implementedgraphical pipeline models. Examples of such APIs include the OpenGraphics Library (OPENGL) API and the METAL API. The graphicalprocessing pipeline includes a number of stages to convert a group ofvertices, textures, buffers, and state information into an image frameon the screen. In an implementation, one of the stages of the graphicalprocessing pipeline is a shader, which may be utilized as part of aparticular augmented reality content generator that is applied to aninput frame (e.g., image or video). A shader can be implemented as coderunning on a specialized processing unit, also referred to as a shaderunit or shader processor, usually executing several computing threads,programmed to generate appropriate levels of color and/or specialeffects to fragments being rendered. For example, a vertex shaderprocesses attributes (position, texture coordinates, color, etc.) of avertex, and a pixel shader processes attributes (texture values, color,z-depth and alpha value) of a pixel. In some instances, a pixel shaderis referred to as a fragment shader.

It is to be appreciated that other types of shader processes may beprovided. In an example, a particular sampling rate is utilized, withinthe graphical processing pipeline, for rendering an entire frame, and/orpixel shading is performed at a particular per-pixel rate. In thismanner, a given electronic device (e.g., the client device 102) operatesthe graphical processing pipeline to convert information correspondingto objects into a bitmap that can be displayed by the electronic device.

The content recording module 610 sends a request(s) to the camera module602 to initiate recording of image data by one or more cameras providedby the client device 102. In an embodiment, the camera module 602 actsas intermediary between other components in the AR content recordingsystem. For example, the camera module can receive a request from thecontent recording module 610 to initiate recording, and forward therequest to the capture module 604 for processing. The capture module604, upon receiving the request from the camera module 602, performsoperations to initiate image data capture by the camera(s) provided bythe client device 102. Captured image data, including timestampinformation for each frame from the captured image data, can then besent to the content recording module 610 for processing. In an example,the content recording module 610 can perform operations to processcaptured image data for rendering by the rendering module 608.

In an embodiment, components of the AR content system 600 cancommunicate using an inter-process communication (IPC) protocol. In anembodiment, components of the AR content system 600 can communicatethrough an API provided by the AR content system 600.

In an embodiment, the camera module 602 receives a signal or command (ora request) to stop recording of image data (e.g., sent from the contentrecording module 610). In response, the camera module 602 sends arequest to the capture module 604 to stop capturing image data. Thecapture module 604, in response to the request to stop recording,complies with the request and ceases further operations to capture imagedata using one or more cameras of the client device 102. The cameramodule 602, after receiving the signal or command to stop recording, canalso asynchronously send a signal to the image data processing module606 that recording of image data (e.g., capture of image data by thecapture module 604) has (requested to be) stopped. The image dataprocessing module 606, after receiving the signal, performs operationsto complete or finish image processing operations, including performingoperations to generate metadata related to AR contents and AR effects.Such metadata can then be sent to the capture module 604, which thengenerates a composite AR content, including the metadata. The compositeAR content can be received by the rendering module 608 and rendered fordisplay on a display device provided by the client device 102.

As mentioned herein, the subject technology enables operations (e.g.,image processing operations) related to facial animation synthesis asdescribed by the following. Some embodiments of the disclosure may allowtaking a source media content (e.g., image, video, and the like) of afirst person (“source actor”) and setting target photos (or video) of asecond entity (hereinafter called “target actor” or “target entity” suchas a second person as an input, and synthesizing animation of the targetactor with facial mimics and head movements of the source actor. Thesubject technology enables the target actor to be animated and therebymimic movements and facial expressions of the source actor. In anembodiment, the subject technology may be utilized in an entertainmentor advertisement context where a user takes a selfie (e.g., mediacontent comprising image or video) and the subject technology can selecta scenario of animating the person and applying visual effects. Thescenarios have different settings and source actor movements, which aretransferred to the media content corresponding to the user selfie. Theresulting media content (e.g., AR content including facial animationsynthesis) can feature the user in different situations and locations.The user can share the AR content including facial animation synthesiswith other users (e.g., friends). Additionally, the AR content includingfacial animation synthesis can be utilized as stickers (e.g., mediaoverlay include AR content) in messaging applications (e.g., messagingclient application 104) or social networking services (e.g., socialnetwork system 122), or as content for an online advertisement to bedisplayed in various situations as described further herein.

In some embodiments, the subject system can manipulate or modify thetarget face based on facial expressions of the source face by performingfacial synthesis operations that enable a real-time mimicking ofpositions of the head of the source actor and facial expressions of thesource actor. Further, in some embodiments, a technical improvement ofthe operation of a computing device includes significantly reducing acomputation time for generating an AR content in which a face of thetarget entity mimics positions of the head of the source actor andfacial expressions of the source actor and allow performing thisgeneration of the AR content on a mobile device, which may have limitedcomputing resources.

In some embodiments, the client device 102 can be configured to displaya target media content (e.g., image or video). The target media contentmay include at least one frame including a face of a target entity inwhich to apply facial synthesis based on the face of the source actor(e.g., the user). In some embodiments, the target media content caninclude a single image (e.g., still or static image instead of a video).In some embodiments, the target media content can be pre-recorded andstored in a memory of the client device 102 or in a cloud-basedcomputing resource to which the client device 102 is communicativelycoupled to such as the messaging server system 108.

In an example, the camera module 602 can capture a source video, via,for example, the camera of the client device 102. The source video mayinclude at least a face of the user (e.g., “source face”), and can bestored in the memory of the client device 102.

According to some embodiments, the client device 102 (e.g., image dataprocessing module 606) or the messaging server system 108 can beconfigured to analyze stored images (e.g., a single image or multipleframes of a source video) of a given user in order to extract facialparameters of the user. The client device 102 or the messaging serversystem 108 can be further configured to modify a target video byreplacing, based on the facial parameters of the user, the target facein the target video with the face of the user utilizing facial synthesistechniques.

Similarly, the client device 102 or the messaging server system 108 canbe configured to analyze the stored images of the user to extract facialparameters of another individual (for example, a friend of the user).The client device 102 can be further configured to modify the targetvideo by replacing, based on the facial parameters of the individual,the target face in the target video with the face of the individualutilizing facial synthesis techniques.

As mentioned herein, such facial synthesis techniques can include atleast, for example, determining facial expressions and a head pose of asource actor, determining facial landmarks of the source actor andreplacing identity parameters of the source actor with identityparameters of the target actor, utilizing machine learning modelsincluding neural networks, and generating a frame sequence (e.g., avideo) of a realistic and plausible-looking head of the target actorwhich moves and express emotions (e.g., facial expressions or facialmovements) that were extracted from the source actor.

Embodiments of the subject technology describe herein provide augmentedreality content including facial synthesis, which can also includeapplying a selected facial expression to a target face, for presentingand sharing with online communities (e.g., social networks and onlinefriends).

FIG. 7 illustrates example interfaces (e.g., graphical user interface)in the subject messaging system, according to some embodiments. In anembodiment, interface 700 and interface 750 are provided by themessaging client application 104 and/or the messaging server system 108,and accessible by the client device 102 to present to a user on adisplay screen of the client device 102.

As shown, interface 700 includes a library of source media content(e.g., video or photo) which are arranged and represented ascorresponding thumbnail images. Each thumbnail image is a selectablegraphical item that can receive user input for selection.

Upon selection of a particular thumbnail, interface 750 is presentedwhich includes a view of the selected media content. As further shown,interface 750 includes graphical items in order to edit (e.g., trim) themedia content. The media content in this example corresponds to a sourcevideo include a head of a source actor and facial expressions of thesource actor in frames of the source video.

FIG. 8 illustrates example interfaces (e.g., graphical user interface)in the subject messaging system, according to some embodiments. In anembodiment, interface 800 and interface 850 are provided by themessaging client application 104 and/or the messaging server system 108,and accessible by the client device 102 to present to a user on adisplay screen of the client device 102.

In some embodiments, the subject technology provides interface 800 thatenables a user to capture (e.g., record) an image or video including aselfie (e.g., user's face) with facial expressions and lip movements inorder to match or mimic the facial expressions found in the sourcevideo.

Additionally, interface 850 is provided to enable the user to select aparticular facial expression among a library of facial expressions(e.g., set of facial expressions including various emotive propertiesfor facial animation and facial synthesis) which are provided asrespective selectable graphical items in interface 850. In this example,when one of the graphical items, corresponding to a particular facialexpression, is selected, the subject technology can modify arepresentation of the target face from the captured image data (e.g.,captured using interface 800 described above), utilizing facialsynthesis techniques, to mimic at least one of positions of a head of asource actor and at least one of facial expressions of the source actorin frames of a source media content.

FIG. 9 illustrates example interfaces (e.g., graphical user interface)in the subject messaging system, according to some embodiments. In anembodiment, interface 900 and interface 950 are provided by themessaging client application 104 and/or the messaging server system 108,and accessible by the client device 102 to present to a user on adisplay screen of the client device 102.

As shown in interface 900, the subject technology generates augmentedreality content including at least a representation of the target facehas been modified, utilizing facial synthesis techniques, to mimic atleast one of positions of a head of a source actor and at least one offacial expressions of the source actor in frames of a source mediacontent.

As shown in interface 950, a title can be inputted and cover thumbnailimage can be selected for the generated augmented reality content frominterface 900, and a selectable graphical item is provided to publishthe content to an online community such as a given social network(s).

FIG. 10 illustrates an interface 1000 (e.g., graphical user interface)in the subject messaging system, according to some embodiments. In anembodiment, the interface 1000 is provided by the messaging clientapplication 104 and/or the messaging server system 108, and accessibleby the client device 102 to present to a user on a display screen of theclient device 102.

After publication, the generated augmented reality content is providedin interface 1000 corresponding to a profile of a user. In this manner,other users in the online community can view the generated augmentedreality content and subscribe to the user's account to receivenotifications of other content from the user.

FIG. 11 is a flowchart illustrating a method 1100, according to certainexample embodiments. The method 1100 may be embodied incomputer-readable instructions for execution by one or more computerprocessors such that the operations of the method 1100 may be performedin part or in whole by the client device 102, particularly with respectto respective components of the AR content system 600 described above inFIG. 6 ; accordingly, the method 1100 is described below by way ofexample with reference thereto. However, it shall be appreciated that atleast some of the operations of the method 1100 may be deployed onvarious other hardware configurations and the method 1100 is notintended to be limited to the AR content system 600.

At operation 1102, the capture module 604 captures image data by aclient device, the captured image data comprising a target face of atarget actor and facial expressions of the target actor, the facialexpressions of the target actor including lip movements.

At operation 1104, the image data processing module 606 receives aselection of a particular facial expression from a set of facialexpressions.

At operation 1106, the image data processing module 606 generates, basedat least in part on frames of a source media content, sets of sourcepose parameters, the sets of the source pose parameters comprisingpositions of representations of a head of a source actor and facialexpressions of the source actor in the frames of the source mediacontent.

At operation 1108, the image data processing module 606 generates, basedat least in part on sets of the source pose parameters and the selectionof the particular facial expression, an output media content, each frameof the output media content including an image of the target face, fromthe captured image data, in at least one frame of the output mediacontent, the image of the target face being modified based on at leastone of the sets of the source pose parameters to mimic at least one ofpositions of the head of the source actor in the frames of the sourcemedia content and at least the particular facial expression from the setof facial expressions.

At operation 1110, the rendering module 608 provides augmented realitycontent based at least in part on the output media content for displayon a computing device.

In an embodiment, the image data processing module 606 provides fordisplay a set of selectable graphical items, each of the selectablegraphical items comprising a particular representation of a particularfacial expression among a set of facial expressions including variousemotive properties for facial animation and facial synthesis; receivinga selection of a particular selectable graphical item from the set ofselectable graphical items; determining the particular facial expressioncorresponding to the particular selectable graphical item; andperforming a modification of the representations of the head of thetarget actor and the facial expressions of the target actor in thecaptured image data based on the particular facial expression.

In an embodiment, performing the modification of the representations ofthe head of the target actor and the facial expressions of the targetactor comprises: the image data processing module 606 receiving a firstlatent vector corresponding to a first set of hidden features of thehead of the source actor and facial expressions of the source actor,wherein the first set of hidden features are not directly observable;and receiving a second latent vector corresponding to a second set ofhidden features of the target face of the target actor and facialexpressions of the target actor, wherein the second set of hiddenfeatures are not directly observable.

In an embodiment, the image data processing module 606 generates, usinga mapping deep neural network, a first intermediate latent vector basedon the first latent vector, wherein the mapping deep neural networkapplies a non-linear function to the first latent vector through morethan three layers, the first intermediate latent vector includes a firstset of styles associated with the particular facial expression based onthe head of the source actor and facial expressions of the source actor,and generates, using the mapping deep neural network, a secondintermediate latent vector based on the second latent vector, whereinthe mapping deep neural network applies the non-linear function to thesecond latent vector through more than the three layers, the secondintermediate latent vector includes a second set of styles associatedwith the head of the target actor and facial expressions of the targetactor.

In an embodiment, the image data processing module 606 combines, using asynthesis convolutional neural network, a first amount of the first setof styles and a second amount of the second set of styles to generate acombined set of styles; and applies the combined set of styles to thecaptured image data comprising the target face of the target actor andfacial expressions of the target actor to generate a set of frames ofthe output media content.

In an embodiment, the first set of styles includes a first set of coarseresolution styles, a second set of medium resolution styles, and a thirdset of fine resolution styles, the coarse resolution styles have a lowerresolution the medium resolution styles, and the medium resolutionstyles have a lower resolution than the fine resolution styles.

In an embodiment, applying the combined set of styles comprises: theimage data processing module 606 combining the second set of mediumresolution styles with the first amount of the first set of styles; andapplying the combined second set of medium resolution styles and thefirst amount of the first set of styles to generate the set of frames ofthe output media content.

In an embodiment, the set of frames of the output media content includesa representation of the particular facial expression.

In an embodiment, a combination of the mapping deep neural network andthe synthesis convolutional neural network comprise a generativeadversarial network (GAN).

In an embodiment, the set of facial expressions includes differentfacial expressions corresponding to a respective facial expressionrepresenting visual depictions to make the source actor appearconfident, sad, excited, thinking, neutral, positive, angry, worried,surprised, spooky, shouting, or shy.

FIG. 12 is a block diagram illustrating an example software architecture1206, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 12 is a non-limiting example of asoftware architecture and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1206 may execute on hardwaresuch as machine 1300 of FIG. 13 that includes, among other things,processors 1304, memory 1314, and (input/output) I/O components 1318. Arepresentative hardware layer 1252 is illustrated and can represent, forexample, the machine 1300 of FIG. 13 . The representative hardware layer1252 includes a processing unit 1254 having associated executableinstructions 1204. Executable instructions 1204 represent the executableinstructions of the software architecture 1206, including implementationof the methods, components, and so forth described herein. The hardwarelayer 1252 also includes memory and/or storage modules memory/storage1256, which also have executable instructions 1204. The hardware layer1252 may also comprise other hardware 1258.

In the example architecture of FIG. 12 , the software architecture 1206may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1206may include layers such as an operating system 1202, libraries 1220,frameworks/middleware 1218, applications 1216, and a presentation layer1214. Operationally, the applications 1216 and/or other componentswithin the layers may invoke API calls 1208 through the software stackand receive a response as in messages 1212 to the API calls 1208. Thelayers illustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile or specialpurpose operating systems may not provide a frameworks/middleware 1218,while others may provide such a layer. Other software architectures mayinclude additional or different layers.

The operating system 1202 may manage hardware resources and providecommon services. The operating system 1202 may include, for example, akernel 1222, services 1224, and drivers 1226. The kernel 1222 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1222 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1224 may provideother common services for the other software layers. The drivers 1226are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1226 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1220 provide a common infrastructure that is used by theapplications 1216 and/or other components and/or layers. The libraries1220 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 1202 functionality (e.g., kernel 1222,services 1224 and/or drivers 1226). The libraries 1220 may includesystem libraries 1244 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1220 may include API libraries 1246 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to render 2D and3D in a graphic content on a display), database libraries (e.g., SQLitethat may provide various relational database functions), web libraries(e.g., WebKit that may provide web browsing functionality), and thelike. The libraries 1220 may also include a wide variety of otherlibraries 1248 to provide many other APIs to the applications 1216 andother software components/modules.

The frameworks/middleware 1218 (also sometimes referred to asmiddleware) provide a higher-level common infrastructure that may beused by the applications 1216 and/or other software components/modules.For example, the frameworks/middleware 1218 may provide various graphicuser interface (GUI) functions, high-level resource management,high-level location services, and so forth. The frameworks/middleware1218 may provide a broad spectrum of other APIs that may be used by theapplications 1216 and/or other software components/modules, some ofwhich may be specific to a particular operating system 1202 or platform.

The applications 1216 include built-in applications 1238 and/orthird-party applications 1240. Examples of representative built-inapplications 1238 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 1240 may include anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1240 may invoke the API calls 1208 provided bythe mobile operating system (such as operating system 1202) tofacilitate functionality described herein.

The applications 1216 may use built in operating system functions (e.g.,kernel 1222, services 1224 and/or drivers 1226), libraries 1220, andframeworks/middleware 1218 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systemsinteractions with a user may occur through a presentation layer, such aspresentation layer 1214. In these systems, the application/component‘logic’ can be separated from the aspects of the application/componentthat interact with a user.

FIG. 13 is a block diagram illustrating components of a machine 1300,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 13 shows a diagrammatic representation of the machine1300 in the example form of a computer system, within which instructions1310 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1300 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1310 may be used to implement modules or componentsdescribed herein. The instructions 1310 transform the general,non-programmed machine 1300 into a particular machine 1300 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1300 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1300 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1300 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1310, sequentially or otherwise, that specify actions to betaken by machine 1300. Further, while only a single machine 1300 isillustrated, the term ‘machine’ shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1310 to perform any one or more of the methodologiesdiscussed herein.

The machine 1300 may include processors 1304, including processor 1308to processor 1312, memory/storage 1306, and I/O components 1318, whichmay be configured to communicate with each other such as via a bus 1302.The memory/storage 1306 may include a memory 1314, such as a mainmemory, or other memory storage, and a storage unit 1316, bothaccessible to the processors 1304 such as via the bus 1302. The storageunit 1316 and memory 1314 store the instructions 1310 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1310 may also reside, completely or partially, within thememory 1314, within the storage unit 1316, within at least one of theprocessors 1304 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine1300. Accordingly, the memory 1314, the storage unit 1316, and thememory of processors 1304 are examples of machine-readable media.

The I/O components 1318 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1318 that are included in a particular machine 1300 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1318 may include many other components that are not shown inFIG. 13 . The I/O components 1318 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1318may include output components 1326 and input components 1328. The outputcomponents 1326 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1328 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1318 may includebiometric components 1330, motion components 1334, environmentalcomponents 1336, or position components 1338 among a wide array of othercomponents. For example, the biometric components 1330 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1334 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1336 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1338 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1318 may include communication components 1340operable to couple the machine 1300 to a network 1332 or devices 1320via coupling 1324 and coupling 1322, respectively. For example, thecommunication components 1340 may include a network interface componentor other suitable device to interface with the network 1332. In furtherexamples, communication components 1340 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1320 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1340 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1340 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1340, such as, location via Internet Protocol (IP) geo-location,location via Wi-Fi® signal triangulation, location via detecting a NFCbeacon signal that may indicate a particular location, and so forth.

The following discussion relates to various terms or phrases that arementioned throughout the subject disclosure.

-   -   ‘Signal Medium’ refers to any intangible medium that is capable        of storing, encoding, or carrying the instructions for execution        by a machine and includes digital or analog communications        signals or other intangible media to facilitate communication of        software or data. The term ‘signal medium’ shall be taken to        include any form of a modulated data signal, carrier wave, and        so forth. The term ‘modulated data signal’ means a signal that        has one or more of its characteristics set or changed in such a        matter as to encode information in the signal. The terms        ‘transmission medium’ and ‘signal medium’ mean the same thing        and may be used interchangeably in this disclosure.    -   ‘Communication Network’ refers to one or more portions of a        network that may be an ad hoc network, an intranet, an extranet,        a virtual private network (VPN), a local area network (LAN), a        wireless LAN (WLAN), a wide area network (WAN), a wireless WAN        (WWAN), a metropolitan area network (MAN), the Internet, a        portion of the Internet, a portion of the Public Switched        Telephone Network (PSTN), a plain old telephone service (POTS)        network, a cellular telephone network, a wireless network, a        Wi-Fi® network, another type of network, or a combination of two        or more such networks. For example, a network or a portion of a        network may include a wireless or cellular network and the        coupling may be a Code Division Multiple Access (CDMA)        connection, a Global System for Mobile communications (GSM)        connection, or other types of cellular or wireless coupling. In        this example, the coupling may implement any of a variety of        types of data transfer technology, such as Single Carrier Radio        Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO)        technology, General Packet Radio Service (GPRS) technology,        Enhanced Data rates for GSM Evolution (EDGE) technology, third        Generation Partnership Project (3GPP) including 3G, fourth        generation wireless (4G) networks, Universal Mobile        Telecommunications System (UMTS), High Speed Packet Access        (HSPA), Worldwide Interoperability for Microwave Access (WiMAX),        Long Term Evolution (LTE) standard, others defined by various        standard-setting organizations, other long-range protocols, or        other data transfer technology.    -   ‘Processor’ refers to any circuit or virtual circuit (a physical        circuit emulated by logic executing on an actual processor) that        manipulates data values according to control signals (e.g.,        ‘commands’, ‘op codes’, ‘machine code’, etc.) and which produces        corresponding output signals that are applied to operate a        machine. A processor may, for example, be a Central Processing        Unit (CPU), a Reduced Instruction Set Computing (RISC)        processor, a Complex Instruction Set Computing (CISC) processor,        a Graphics Processing Unit (GPU), a Digital Signal Processor        (DSP), an Application Specific Integrated Circuit (ASIC), a        Radio-Frequency Integrated Circuit (RFIC) or any combination        thereof. A processor may further be a multi-core processor        having two or more independent processors (sometimes referred to        as ‘cores’) that may execute instructions contemporaneously.    -   ‘Machine-Storage Medium’ refers to a single or multiple storage        devices and/or media (e.g., a centralized or distributed        database, and/or associated caches and servers) that store        executable instructions, routines and/or data. The term shall        accordingly be taken to include, but not be limited to,        solid-state memories, and optical and magnetic media, including        memory internal or external to processors. Specific examples of        machine-storage media, computer-storage media and/or        device-storage media include non-volatile memory, including by        way of example semiconductor memory devices, e.g., erasable        programmable read-only memory (EPROM), electrically erasable        programmable read-only memory (EEPROM), FPGA, and flash memory        devices; magnetic disks such as internal hard disks and        removable disks; magneto-optical disks; and CD-ROM and DVD-ROM        disks The terms ‘machine-storage medium,’ ‘device-storage        medium,’ ‘computer-storage medium’ mean the same thing and may        be used interchangeably in this disclosure. The terms        ‘machine-storage media,’ ‘computer-storage media,’ and        ‘device-storage media’ specifically exclude carrier waves,        modulated data signals, and other such media, at least some of        which are covered under the term ‘signal medium.’    -   ‘Component’ refers to a device, physical entity, or logic having        boundaries defined by function or subroutine calls, branch        points, APIs, or other technologies that provide for the        partitioning or modularization of particular processing or        control functions. Components may be combined via their        interfaces with other components to carry out a machine process.        A component may be a packaged functional hardware unit designed        for use with other components and a part of a program that        usually performs a particular function of related functions.        Components may constitute either software components (e.g., code        embodied on a machine-readable medium) or hardware components. A        ‘hardware component’ is a tangible unit capable of performing        certain operations and may be configured or arranged in a        certain physical manner. In various example embodiments, one or        more computer systems (e.g., a standalone computer system, a        client computer system, or a server computer system) or one or        more hardware components of a computer system (e.g., a processor        or a group of processors) may be configured by software (e.g.,        an application or application portion) as a hardware component        that operates to perform certain operations as described herein.        A hardware component may also be implemented mechanically,        electronically, or any suitable combination thereof. For        example, a hardware component may include dedicated circuitry or        logic that is permanently configured to perform certain        operations. A hardware component may be a special-purpose        processor, such as a field-programmable gate array (FPGA) or an        application specific integrated circuit (ASIC). A hardware        component may also include programmable logic or circuitry that        is temporarily configured by software to perform certain        operations. For example, a hardware component may include        software executed by a general-purpose processor or other        programmable processor. Once configured by such software,        hardware components become specific machines (or specific        components of a machine) uniquely tailored to perform the        configured functions and are no longer general-purpose        processors. It will be appreciated that the decision to        implement a hardware component mechanically, in dedicated and        permanently configured circuitry, or in temporarily configured        circuitry (e.g., configured by software), may be driven by cost        and time considerations. Accordingly, the phrase ‘hardware        component’(or ‘hardware-implemented component’) should be        understood to encompass a tangible entity, be that an entity        that is physically constructed, permanently configured (e.g.,        hardwired), or temporarily configured (e.g., programmed) to        operate in a certain manner or to perform certain operations        described herein. Considering embodiments in which hardware        components are temporarily configured (e.g., programmed), each        of the hardware components need not be configured or        instantiated at any one instance in time. For example, where a        hardware component comprises a general-purpose processor        configured by software to become a special-purpose processor,        the general-purpose processor may be configured as respectively        different special-purpose processors (e.g., comprising different        hardware components) at different times. Software accordingly        configures a particular processor or processors, for example, to        constitute a particular hardware component at one instance of        time and to constitute a different hardware component at a        different instance of time. Hardware components can provide        information to, and receive information from, other hardware        components. Accordingly, the described hardware components may        be regarded as being communicatively coupled. Where multiple        hardware components exist contemporaneously, communications may        be achieved through signal transmission (e.g., over appropriate        circuits and buses) between or among two or more of the hardware        components. In embodiments in which multiple hardware components        are configured or instantiated at different times,        communications between such hardware components may be achieved,        for example, through the storage and retrieval of information in        memory structures to which the multiple hardware components have        access. For example, one hardware component may perform an        operation and store the output of that operation in a memory        device to which it is communicatively coupled. A further        hardware component may then, at a later time, access the memory        device to retrieve and process the stored output. Hardware        components may also initiate communications with input or output        devices, and can operate on a resource (e.g., a collection of        information). The various operations of example methods        described herein may be performed, at least partially, by one or        more processors that are temporarily configured (e.g., by        software) or permanently configured to perform the relevant        operations. Whether temporarily or permanently configured, such        processors may constitute processor-implemented components that        operate to perform one or more operations or functions described        herein. As used herein, ‘processor-implemented component’ refers        to a hardware component implemented using one or more        processors. Similarly, the methods described herein may be at        least partially processor-implemented, with a particular        processor or processors being an example of hardware. For        example, at least some of the operations of a method may be        performed by one or more processors or processor-implemented        components. Moreover, the one or more processors may also        operate to support performance of the relevant operations in a        ‘cloud computing’ environment or as a ‘software as a service’        (SaaS). For example, at least some of the operations may be        performed by a group of computers (as examples of machines        including processors), with these operations being accessible        via a network (e.g., the Internet) and via one or more        appropriate interfaces (e.g., an API). The performance of        certain of the operations may be distributed among the        processors, not only residing within a single machine, but        deployed across a number of machines. In some example        embodiments, the processors or processor-implemented components        may be located in a single geographic location (e.g., within a        home environment, an office environment, or a server farm). In        other example embodiments, the processors or        processor-implemented components may be distributed across a        number of geographic locations.    -   ‘Carrier Signal’ refers to any intangible medium that is capable        of storing, encoding, or carrying instructions for execution by        the machine, and includes digital or analog communications        signals or other intangible media to facilitate communication of        such instructions. Instructions may be transmitted or received        over a network using a transmission medium via a network        interface device.    -   ‘Computer-Readable Medium’ refers to both machine-storage media        and transmission media. Thus, the terms include both storage        devices/media and carrier waves/modulated data signals. The        terms ‘machine-readable medium,’ ‘computer-readable medium’ and        ‘device-readable medium’ mean the same thing and may be used        interchangeably in this disclosure.    -   ‘Client Device’ refers to any machine that interfaces to a        communications network to obtain resources from one or more        server systems or other client devices. A client device may be,        but is not limited to, a mobile phone, desktop computer, laptop,        portable digital assistants (PDAs), smartphones, tablets,        ultrabooks, netbooks, laptops, multi-processor systems,        microprocessor-based or programmable consumer electronics, game        consoles, set-top boxes, or any other communication device that        a user may use to access a network. In the subject disclosure, a        client device is also referred to as an ‘electronic device.’    -   ‘Ephemeral Message’ refers to a message that is accessible for a        time-limited duration. An ephemeral message may be a text, an        image, a video and the like. The access time for the ephemeral        message may be set by the message sender. Alternatively, the        access time may be a default setting or a setting specified by        the recipient. Regardless of the setting technique, the message        is transitory.    -   ‘Signal Medium’ refers to any intangible medium that is capable        of storing, encoding, or carrying the instructions for execution        by a machine and includes digital or analog communications        signals or other intangible media to facilitate communication of        software or data. The term ‘signal medium’ shall be taken to        include any form of a modulated data signal, carrier wave, and        so forth. The term ‘modulated data signal’ means a signal that        has one or more of its characteristics set or changed in such a        matter as to encode information in the signal. The terms        ‘transmission medium’ and ‘signal medium’ mean the same thing        and may be used interchangeably in this disclosure.    -   ‘Communication Network’ refers to one or more portions of a        network that may be an ad hoc network, an intranet, an extranet,        a virtual private network (VPN), a local area network (LAN), a        wireless LAN (WLAN), a wide area network (WAN), a wireless WAN        (WWAN), a metropolitan area network (MAN), the Internet, a        portion of the Internet, a portion of the Public Switched        Telephone Network (PSTN), a plain old telephone service (POTS)        network, a cellular telephone network, a wireless network, a        Wi-Fi® network, another type of network, or a combination of two        or more such networks. For example, a network or a portion of a        network may include a wireless or cellular network and the        coupling may be a Code Division Multiple Access (CDMA)        connection, a Global System for Mobile communications (GSM)        connection, or other types of cellular or wireless coupling. In        this example, the coupling may implement any of a variety of        types of data transfer technology, such as Single Carrier Radio        Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO)        technology, General Packet Radio Service (GPRS) technology,        Enhanced Data rates for GSM Evolution (EDGE) technology, third        Generation Partnership Project (3GPP) including 3G, fourth        generation wireless (4G) networks, Universal Mobile        Telecommunications System (UMTS), High Speed Packet Access        (HSPA), Worldwide Interoperability for Microwave Access (WiMAX),        Long Term Evolution (LTE) standard, others defined by various        standard-setting organizations, other long-range protocols, or        other data transfer technology.    -   ‘Processor’ refers to any circuit or virtual circuit (a physical        circuit emulated by logic executing on an actual processor) that        manipulates data values according to control signals (e.g.,        ‘commands’, ‘op codes’, ‘machine code’, etc.) and which produces        corresponding output signals that are applied to operate a        machine. A processor may, for example, be a Central Processing        Unit (CPU), a Reduced Instruction Set Computing (RISC)        processor, a Complex Instruction Set Computing (CISC) processor,        a Graphics Processing Unit (GPU), a Digital Signal Processor        (DSP), an Application Specific Integrated Circuit (ASIC), a        Radio-Frequency Integrated Circuit (RFIC) or any combination        thereof. A processor may further be a multi-core processor        having two or more independent processors (sometimes referred to        as ‘cores’) that may execute instructions contemporaneously.    -   ‘Machine-Storage Medium’ refers to a single or multiple storage        devices and/or media (e.g., a centralized or distributed        database, and/or associated caches and servers) that store        executable instructions, routines and/or data. The term shall        accordingly be taken to include, but not be limited to,        solid-state memories, and optical and magnetic media, including        memory internal or external to processors. Specific examples of        machine-storage media, computer-storage media and/or        device-storage media include non-volatile memory, including by        way of example semiconductor memory devices, e.g., erasable        programmable read-only memory (EPROM), electrically erasable        programmable read-only memory (EEPROM), FPGA, and flash memory        devices; magnetic disks such as internal hard disks and        removable disks; magneto-optical disks; and CD-ROM and DVD-ROM        disks The terms ‘machine-storage medium,’ ‘device-storage        medium,’ ‘computer-storage medium’ mean the same thing and may        be used interchangeably in this disclosure. The terms        ‘machine-storage media,’ ‘computer-storage media,’ and        ‘device-storage media’ specifically exclude carrier waves,        modulated data signals, and other such media, at least some of        which are covered under the term ‘signal medium.’    -   ‘Component’ refers to a device, physical entity, or logic having        boundaries defined by function or subroutine calls, branch        points, APIs, or other technologies that provide for the        partitioning or modularization of particular processing or        control functions. Components may be combined via their        interfaces with other components to carry out a machine process.        A component may be a packaged functional hardware unit designed        for use with other components and a part of a program that        usually performs a particular function of related functions.        Components may constitute either software components (e.g., code        embodied on a machine-readable medium) or hardware components. A        ‘hardware component’ is a tangible unit capable of performing        certain operations and may be configured or arranged in a        certain physical manner. In various example embodiments, one or        more computer systems (e.g., a standalone computer system, a        client computer system, or a server computer system) or one or        more hardware components of a computer system (e.g., a processor        or a group of processors) may be configured by software (e.g.,        an application or application portion) as a hardware component        that operates to perform certain operations as described herein.        A hardware component may also be implemented mechanically,        electronically, or any suitable combination thereof. For        example, a hardware component may include dedicated circuitry or        logic that is permanently configured to perform certain        operations. A hardware component may be a special-purpose        processor, such as a field-programmable gate array (FPGA) or an        application specific integrated circuit (ASIC). A hardware        component may also include programmable logic or circuitry that        is temporarily configured by software to perform certain        operations. For example, a hardware component may include        software executed by a general-purpose processor or other        programmable processor. Once configured by such software,        hardware components become specific machines (or specific        components of a machine) uniquely tailored to perform the        configured functions and are no longer general-purpose        processors. It will be appreciated that the decision to        implement a hardware component mechanically, in dedicated and        permanently configured circuitry, or in temporarily configured        circuitry (e.g., configured by software), may be driven by cost        and time considerations. Accordingly, the phrase ‘hardware        component’(or ‘hardware-implemented component’) should be        understood to encompass a tangible entity, be that an entity        that is physically constructed, permanently configured (e.g.,        hardwired), or temporarily configured (e.g., programmed) to        operate in a certain manner or to perform certain operations        described herein. Considering embodiments in which hardware        components are temporarily configured (e.g., programmed), each        of the hardware components need not be configured or        instantiated at any one instance in time. For example, where a        hardware component comprises a general-purpose processor        configured by software to become a special-purpose processor,        the general-purpose processor may be configured as respectively        different special-purpose processors (e.g., comprising different        hardware components) at different times. Software accordingly        configures a particular processor or processors, for example, to        constitute a particular hardware component at one instance of        time and to constitute a different hardware component at a        different instance of time. Hardware components can provide        information to, and receive information from, other hardware        components. Accordingly, the described hardware components may        be regarded as being communicatively coupled. Where multiple        hardware components exist contemporaneously, communications may        be achieved through signal transmission (e.g., over appropriate        circuits and buses) between or among two or more of the hardware        components. In embodiments in which multiple hardware components        are configured or instantiated at different times,        communications between such hardware components may be achieved,        for example, through the storage and retrieval of information in        memory structures to which the multiple hardware components have        access. For example, one hardware component may perform an        operation and store the output of that operation in a memory        device to which it is communicatively coupled. A further        hardware component may then, at a later time, access the memory        device to retrieve and process the stored output. Hardware        components may also initiate communications with input or output        devices, and can operate on a resource (e.g., a collection of        information). The various operations of example methods        described herein may be performed, at least partially, by one or        more processors that are temporarily configured (e.g., by        software) or permanently configured to perform the relevant        operations. Whether temporarily or permanently configured, such        processors may constitute processor-implemented components that        operate to perform one or more operations or functions described        herein. As used herein, ‘processor-implemented component’ refers        to a hardware component implemented using one or more        processors. Similarly, the methods described herein may be at        least partially processor-implemented, with a particular        processor or processors being an example of hardware. For        example, at least some of the operations of a method may be        performed by one or more processors or processor-implemented        components. Moreover, the one or more processors may also        operate to support performance of the relevant operations in a        ‘cloud computing’ environment or as a ‘software as a service’        (SaaS). For example, at least some of the operations may be        performed by a group of computers (as examples of machines        including processors), with these operations being accessible        via a network (e.g., the Internet) and via one or more        appropriate interfaces (e.g., an API). The performance of        certain of the operations may be distributed among the        processors, not only residing within a single machine, but        deployed across a number of machines. In some example        embodiments, the processors or processor-implemented components        may be located in a single geographic location (e.g., within a        home environment, an office environment, or a server farm). In        other example embodiments, the processors or        processor-implemented components may be distributed across a        number of geographic locations.    -   ‘Carrier Signal’ refers to any intangible medium that is capable        of storing, encoding, or carrying instructions for execution by        the machine, and includes digital or analog communications        signals or other intangible media to facilitate communication of        such instructions. Instructions may be transmitted or received        over a network using a transmission medium via a network        interface device.    -   ‘Computer-Readable Medium’ refers to both machine-storage media        and transmission media. Thus, the terms include both storage        devices/media and carrier waves/modulated data signals. The        terms ‘machine-readable medium,’ ‘computer-readable medium’ and        ‘device-readable medium’ mean the same thing and may be used        interchangeably in this disclosure.    -   ‘Client Device’ refers to any machine that interfaces to a        communications network to obtain resources from one or more        server systems or other client devices. A client device may be,        but is not limited to, a mobile phone, desktop computer, laptop,        portable digital assistants (PDAs), smartphones, tablets,        ultrabooks, netbooks, laptops, multi-processor systems,        microprocessor-based or programmable consumer electronics, game        consoles, set-top boxes, or any other communication device that        a user may use to access a network.    -   ‘Ephemeral Message’ refers to a message that is accessible for a        time-limited duration. An ephemeral message may be a text, an        image, a video and the like. The access time for the ephemeral        message may be set by the message sender. Alternatively, the        access time may be a default setting or a setting specified by        the recipient. Regardless of the setting technique, the message        is transitory.

What is claimed is:
 1. A method, comprising: capturing, by one or morehardware processors, image data by a client device, the captured imagedata comprising a target face of a target actor and facial expressionsof the target actor, the facial expressions of the target actorincluding lip movements; generating, by the one or more hardwareprocessors and based at least in part on frames of a source mediacontent, sets of source pose parameters, the sets of the source poseparameters comprising positions of representations of a head of a sourceactor and facial expressions of the source actor in the frames of thesource media content, the source media content comprising a source videowith the facial expressions of the source actor that are different thanthe captured image data including the facial expressions of the targetactor; providing, by the one or more hardware processors, for display aset of selectable graphical items, the set of selectable graphical itemscomprising different graphical representations of a set of facialexpressions, each of the selectable graphical items comprising aparticular representation of a facial expression among the set of facialexpressions; receiving, by the one or more hardware processors, aselection of a particular selectable graphical item of a particularfacial expression from the different graphical representations of theset of facial expressions; determining the particular facial expressioncorresponding to the particular selectable graphical item that has beenselected; performing a modification of the head of the target actor andthe facial expressions of the target actor in the captured image databased on the particular facial expression; generating, based at least inpart on sets of the source pose parameters and the modification of thehead of the target actor and the facial expressions of the target actorin the captured image data, by the one or more hardware processors, anoutput media content, each frame of the output media content includingan image of the target face, from the captured image data, in at leastone frame of the output media content, the image of the target facebeing modified based on at least one of the sets of the source poseparameters to mimic at least one of positions of the head of the sourceactor in the frames of the source media content and at least theparticular facial expression from the set of facial expressions; andproviding, by the one or more hardware processors, augmented realitycontent based at least in part on the output media content for displayon a computing device.
 2. The method of claim 1, wherein performing themodification of the representations of the head of the target actor andthe facial expressions of the target actor comprises: receiving a firstlatent vector corresponding to a first set of hidden features of thehead of the source actor and facial expressions of the source actor,wherein the first set of hidden features are not directly observable;and receiving a second latent vector corresponding to a second set ofhidden features of the target face of the target actor and facialexpressions of the target actor, wherein the second set of hiddenfeatures are not directly observable.
 3. The method of claim 2, furthercomprising: generating, using a mapping deep neural network, a firstintermediate latent vector based on the first latent vector, wherein themapping deep neural network applies a non-linear function to the firstlatent vector through more than three layers, the first intermediatelatent vector includes a first set of styles associated with theparticular facial expression based on the head of the source actor andfacial expressions of the source actor; and generating, using themapping deep neural network, a second intermediate latent vector basedon the second latent vector, wherein the mapping deep neural networkapplies the non-linear function to the second latent vector through morethan the three layers, the second intermediate latent vector includes asecond set of styles associated with the head of the target actor andfacial expressions of the target actor.
 4. The method of claim 3,further comprising: combining, using a synthesis convolutional neuralnetwork, a first amount of the first set of styles and a second amountof the second set of styles to generate a combined set of styles; andapplying the combined set of styles to the captured image datacomprising the target face of the target actor and facial expressions ofthe target actor to generate a set of frames of the output mediacontent.
 5. The method of claim 4, wherein the first set of stylesincludes a first set of coarse resolution styles, a second set of mediumresolution styles, and a third set of fine resolution styles, the coarseresolution styles have a lower resolution the medium resolution styles,and the medium resolution styles have a lower resolution than the fineresolution styles.
 6. The method of claim 5, wherein applying thecombined set of styles comprises: combining the second set of mediumresolution styles with the first amount of the first set of styles; andapplying the combined second set of medium resolution styles and thefirst amount of the first set of styles to generate the set of frames ofthe output media content.
 7. The method of claim 6, wherein the set offrames of the output media content includes a representation of theparticular facial expression.
 8. The method of claim 4, wherein acombination of the mapping deep neural network and the synthesisconvolutional neural network comprise a generative adversarial network(GAN).
 9. The method of claim 1, wherein the set of facial expressionsincludes different facial expressions corresponding to a respectivefacial expression representing visual depictions to make the sourceactor appear confident, sad, excited, thinking, neutral, positive,angry, worried, surprised, spooky, shouting, or shy.
 10. A systemcomprising: a processor; and a memory including instructions that, whenexecuted by the processor, cause the processor to perform operationscomprising: capturing, by one or more hardware processors, image data bya client device, the captured image data comprising a target face of atarget actor and facial expressions of the target actor, the facialexpressions of the target actor including lip movements; generating, bythe one or more hardware processors and based at least in part on framesof a source media content, sets of source pose parameters, the sets ofthe source pose parameters comprising positions of representations of ahead of a source actor and facial expressions of the source actor in theframes of the source media content, the source media content comprisinga source video with the facial expressions of the source actor that aredifferent than the captured image data including the facial expressionsof the target actor; providing, by the one or more hardware processors,for display a set of selectable graphical items, the set of selectablegraphical items comprising different graphical representations of a setof facial expressions, each of the selectable graphical items comprisinga particular representation of a facial expression among the set offacial expressions; receiving, by the one or more hardware processors, aselection of a particular selectable graphical item of a particularfacial expression from the different graphical representations of theset of facial expressions; determining the particular facial expressioncorresponding to the particular selectable graphical item that has beenselected; performing a modification of the head of the target actor andthe facial expressions of the target actor in the captured image databased on the particular facial expression; generating, based at least inpart on sets of the source pose parameters and the modification of thehead of the target actor and the facial expressions of the target actorin the captured image data, by the one or more hardware processors, anoutput media content, each frame of the output media content includingan image of the target face, from the captured image data, in at leastone frame of the output media content, the image of the target facebeing modified based on at least one of the sets of the source poseparameters to mimic at least one of positions of the head of the sourceactor in the frames of the source media content and at least theparticular facial expression from the set of facial expressions; andproviding, by the one or more hardware processors, augmented realitycontent based at least in part on the output media content for displayon a computing device.
 11. The system of claim 10, wherein performingthe modification of the representations of the head of the target actorand the facial expressions of the target actor comprises: receiving afirst latent vector corresponding to a first set of hidden features ofthe head of the source actor and facial expressions of the source actor,wherein the first set of hidden features are not directly observable;and receiving a second latent vector corresponding to a second set ofhidden features of the target face of the target actor and facialexpressions of the target actor, wherein the second set of hiddenfeatures are not directly observable.
 12. The system of claim 11,wherein the operations further comprise: generating, using a mappingdeep neural network, a first intermediate latent vector based on thefirst latent vector, wherein the mapping deep neural network applies anon-linear function to the first latent vector through more than threelayers, the first intermediate latent vector includes a first set ofstyles associated with the particular facial expression based on thehead of the source actor and facial expressions of the source actor; andgenerating, using the mapping deep neural network, a second intermediatelatent vector based on the second latent vector, wherein the mappingdeep neural network applies the non-linear function to the second latentvector through more than the three layers, the second intermediatelatent vector includes a second set of styles associated with the headof the target actor and facial expressions of the target actor.
 13. Thesystem of claim 12, wherein the operations further comprise: combining,using a synthesis convolutional neural network, a first amount of thefirst set of styles and a second amount of the second set of styles togenerate a combined set of styles; and applying the combined set ofstyles to the captured image data comprising the target face of thetarget actor and facial expressions of the target actor to generate aset of frames of the output media content.
 14. The system of claim 13,wherein the first set of styles includes a first set of coarseresolution styles, a second set of medium resolution styles, and a thirdset of fine resolution styles, the coarse resolution styles have a lowerresolution the medium resolution styles, and the medium resolutionstyles have a lower resolution than the fine resolution styles.
 15. Thesystem of claim 14, wherein applying the combined set of stylescomprises: combining the second set of medium resolution styles with thefirst amount of the first set of styles; and applying the combinedsecond set of medium resolution styles and the first amount of the firstset of styles to generate the set of frames of the output media content.16. The system of claim 15, wherein the set of frames of the outputmedia content includes a representation of the particular facialexpression.
 17. The system of claim 13, wherein a combination of themapping deep neural network and the synthesis convolutional neuralnetwork comprise a generative adversarial network (GAN).
 18. Anon-transitory computer-readable medium comprising instructions, whichwhen executed by a computing device, cause the computing device toperform operations comprising: capturing image data by a client device,the captured image data comprising a target face of a target actor andfacial expressions of the target actor, the facial expressions of thetarget actor including lip movements; generating, based at least in parton frames of a source media content, sets of source pose parameters, thesets of the source pose parameters comprising positions ofrepresentations of a head of a source actor and facial expressions ofthe source actor in the frames of the source media content, the sourcemedia content comprising a source video with the facial expressions ofthe source actor that are different than the captured image dataincluding the facial expressions of the target actor; providing fordisplay a set of selectable graphical items, the set of selectablegraphical items comprising different graphical representations of a setof facial expressions, each of the selectable graphical items comprisinga particular representation of a facial expression among the set offacial expressions; receiving a selection of a particular selectablegraphical item of a particular facial expression from the differentgraphical representations of the set of facial expressions; determiningthe particular facial expression corresponding to the particularselectable graphical item that has been selected; performing amodification of the head of the target actor and the facial expressionsof the target actor in the captured image data based on the particularfacial expression; generating, based at least in part on sets of thesource pose parameters and the modification of the head of the targetactor and the facial expressions of the target actor in the capturedimage data, an output media content, each frame of the output mediacontent including an image of the target face, from the captured imagedata, in at least one frame of the output media content, the image ofthe target face being modified based on at least one of the sets of thesource pose parameters to mimic at least one of positions of the head ofthe source actor in the frames of the source media content and at leastthe particular facial expression from the set of facial expressions; andproviding augmented reality content based at least in part on the outputmedia content for display on a computing device.